Uncertainty measurement of the basic probability assignment function has always been a hot issue in Dempster-Shafer evidence. Many existing studies mainly consider the influence of the mass function itself and the size of the frame of discernment, so that the correlation between the subsets is ignored in the power set of the frame of discernment. Without making full use of the information contained in the evidence, the existing methods are less effective in some cases given in the paper. In this paper, inspired by Shannon entropy and Deng entropy, we propose an improved entropy that not only inherits the many advantages of Shannon entropy and Deng entropy, but also fully considers the relationship between subsets, which makes the improved entropy overcome the shortcomings of existing methods and have greater advantages in uncertainty measurement. Many numerical examples are used to demonstrate the validity and superiority of our proposed entropy in this paper.

中文翻译：

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证据理论中改进的信念熵

基本概率分配函数的不确定性测量一直是 Dempster-Shafer 证据中的热点问题。现有的许多研究主要考虑质量函数本身和判别框架大小的影响，从而在判别框架的幂集中忽略了子集之间的相关性。如果没有充分利用证据中包含的信息，现有方法在论文中给出的某些情况下效果较差。在本文中，受香农熵和邓熵的启发，我们提出了一种改进的熵，它既继承了香农熵和邓熵的许多优点，又充分考虑了子集之间的关系，使改进的熵克服了现有熵的缺点。方法，在不确定度测量方面具有更大的优势。